Water Productivity Mapping (WPM) Using Landsat ETM+ Data for the Irrigated Croplands of the Syrdarya River Basin in Central Asia

The overarching goal of this paper was to espouse methods and protocols for water productivity mapping (WPM) using high spatial resolution Landsat remote sensing data. In a world where land and water for agriculture are becoming increasingly scarce, growing “more crop per drop” (increasing water productivity) becomes crucial for food security of future generations. The study used time-series Landsat ETM+ data to produce WPMs of irrigated crops, with emphasis on cotton in the Galaba study area in the Syrdarya river basin of Central Asia. The WPM methods and protocols using remote sensing data consisted of: (1) crop productivity (ton/ha) maps (CPMs) involving crop type classification, crop yield and biophysical modeling, and extrapolating yield models to larger areas using remotely sensed data; (2) crop water use (m3/ha) maps (WUMs) (or actual seasonal evapotranspiration or actual ET) developed through Simplified Surface Energy Balance (SSEB) model; and (3) water productivity (kg/m3) maps (WPMs) produced by dividing raster layers of CPMs by WUMs. The SSEB model calculated WUMs (actual ET) by multiplying the ET fraction by reference ET. The ET fraction was determined using Landsat thermal imagery by selecting the “hot” pixels (zero ET) and “cold” pixels (maximum ET). The grass reference ET was calculated by FAO Penman-Monteith method using meteorological data. The WPMs for the Galaba study area demonstrated a wide variations (0-0.54 kg/m3) in water productivity of cotton fields with overwhelming proportion (87%) of the area having WP less than 0.30 kg/m3, 11% of the area having WP in range of 0.30-0.36 kg/m3, and only 2% of the area with WP greater than 0.36 kg/m3. These results clearly imply that there are opportunities for significant WP increases in overwhelming proportion of the existing croplands. The areas of low WP are spatially pin-pointed and can be used as focus for WP improvements through better land and water management practices.

[1]  S. Goward,et al.  Vegetation canopy PAR absorptance and the normalized difference vegetation index - An assessment using the SAIL model , 1992 .

[2]  James L. Wright,et al.  Satellite-Based Energy Balance for Mapping Evapotranspiration with Internalized Calibration (METRIC)—Applications , 2007 .

[3]  Hong Yang,et al.  Modeling the role of irrigation in winter wheat yield, crop water productivity, and production in China , 2007, Irrigation Science.

[4]  Prasad S. Thenkabail,et al.  Water productivity mapping methods using remote sensing , 2008 .

[5]  A. Holtslag,et al.  A remote sensing surface energy balance algorithm for land (SEBAL)-1. Formulation , 1998 .

[6]  Wim G.M. Bastiaanssen,et al.  Irrigation Performance Indicators Based on Remotely Sensed Data: a Review of Literature , 1999 .

[7]  W. Bastiaanssen,et al.  A new crop yield forecasting model based on satellite measurements applied across the Indus Basin, Pakistan , 2003 .

[8]  Anthony Morse,et al.  A Landsat-based energy balance and evapotranspiration model in Western US water rights regulation and planning , 2005 .

[9]  Steven R. Evett,et al.  The Bowen ratio-energy balance method for estimating latent heat flux of irrigated alfalfa evaluated in a semi-arid, advective environment , 2000 .

[10]  David Molden,et al.  Accounting for water use and productivity , 1997 .

[11]  Obi Reddy P. Gangalakunta,et al.  Global irrigated area map (GIAM), derived from remote sensing, for the end of the last millennium , 2009 .

[12]  Luis S. Pereira,et al.  FAO-56 Dual Crop Coefficient Method for Estimating Evaporation from Soil and Application Extensions , 2005 .

[13]  P. Thenkabail,et al.  Hyperspectral Vegetation Indices and Their Relationships with Agricultural Crop Characteristics , 2000 .

[14]  A. Piel,et al.  Resonance cone measurements of non-thermal plasma properties in the mid-latitude ionosphere , 1988 .

[15]  R. Hay,et al.  Harvest index: a review of its use in plant breeding and crop physiology , 1995 .

[16]  H. Neckel,et al.  The solar radiation between 3300 and 12500 Å , 1984 .

[17]  W. Bastiaanssen,et al.  Upscaling water productivity in irrigated agriculture using remote-sensing and GIS technologies. , 2003 .

[18]  A. Soltani,et al.  Modeling crop yield using a modified harvest index-based approach: application in chickpea , 2005 .

[19]  M. Ashton,et al.  Accuracy assessments of hyperspectral waveband performance for vegetation analysis applications , 2004 .

[20]  Prasad S. Thenkabail,et al.  Evaluation of Narrowband and Broadband Vegetation Indices for Determining Optimal Hyperspectral Wavebands for Agricultural Crop Characterization , 2002 .

[21]  Marvin E. Jensen,et al.  Development and Evaluation of Evapotranspiration Models for Irrigation Scheduling , 1978 .

[22]  T. Oweis,et al.  Faba bean productivity under rainfed and supplemental irrigation in northern Syria , 2005 .

[23]  C. Tucker,et al.  NASA’s Global Orthorectified Landsat Data Set , 2004 .

[24]  W. Bastiaanssen,et al.  A remote sensing surface energy balance algorithm for land (SEBAL). , 1998 .

[25]  J. Passioura,et al.  Increasing crop productivity when water is scarce--from breeding to field management , 2006 .

[26]  H. Poilvé,et al.  Hyperspectral Imaging and Stress Mapping in Agriculture , 1998 .

[27]  Prasad S. Thenkabail,et al.  Biophysical and yield information for precision farming from near-real-time and historical Landsat TM images , 2003 .

[28]  Reinder A. Feddes,et al.  Assessing crop water productivity from field to regional scale , 2006 .

[29]  Soizik Laguette,et al.  Remote sensing applications for precision agriculture: A learning community approach , 2003 .

[30]  Anju Gaur,et al.  Integrating remote sensing and a process-based hydrological model to evaluate water use and productivity in a south Indian catchment , 2008 .

[31]  Christopher Conrad,et al.  Mapping and assessing water use in a Central Asian irrigation system by utilizing MODIS remote sensing products , 2007 .

[32]  C. Tucker.  Red and photographic infrared linear combinations for monitoring vegetation , 1979 .

[33]  M. S. Moran,et al.  Remote Sensing for Crop Management , 2003 .

[34]  Richard G. Allen,et al.  Satellite-Based Energy Balance for Mapping Evapotranspiration with Internalized Calibration (METRIC)—Model , 2007 .

[35]  Assefa M. Melesse,et al.  A Coupled Remote Sensing and Simplified Surface Energy Balance Approach to Estimate Actual Evapotranspiration from Irrigated Fields , 2007, Sensors (Basel, Switzerland).

[36]  B. Bouman,et al.  Scale effects on water use and water productivity in a rice-based irrigation system (UPRIIS) in the Philippines , 2007 .

[37]  Yann Chemin,et al.  Supplementing Farm-level Water Productivity Assessment by Remote Sensing in Transition Economies , 2005 .

[38]  L. S. Pereira,et al.  Crop evapotranspiration : guidelines for computing crop water requirements , 1998 .

[39]  Jimmy R. Williams,et al.  GEPIC - modelling wheat yield and crop water productivity with high resolution on a global scale , 2007 .

[40]  Pamela L. Nagler,et al.  Evapotranspiration on western U.S. rivers estimated using the Enhanced Vegetation Index from MODIS and data from eddy covariance and Bowen ratio flux towers , 2005 .

[41]  Junguo Liu,et al.  A GIS-based tool for modelling large-scale crop-water relations , 2009, Environ. Model. Softw..

[42]  B. Brisco,et al.  Rice monitoring and production estimation using multitemporal RADARSAT , 2001 .

[43]  I. A. Walter,et al.  The ASCE standardized reference evapotranspiration equation , 2005 .

[44]  Prasad S. Thenkabail,et al.  Thematic mapper vegetation indices for determining soybean and corn growth parameters , 1994 .

[45]  P. Thenkabail,et al.  Within-field wheat yield prediction from IKONOS data: a new matrix approach , 2004 .

[46]  M. Tasumi.  Progress in operational estimation of regional evapotranspiration using satellite imagery , 2003 .

[47]  G. Kite,et al.  Water Productivity from Integrated Basin Modeling , 1999 .

[48]  P. C. Doraiswamya,et al.  Crop condition and yield simulations using Landsat and MODIS , 2004 .

[49]  Trevor W. Tanton,et al.  Remote sensing and GIS for estimation of irrigation crop water demand , 2005 .

[50]  Y.-S. Chung,et al.  Satellite detection of forest fires in Korea and associated smoke plumes , 2003 .

[51]  Ilyas Masih,et al.  Diagnostic analysis of spatial and temporal variations in crop water productivity: A field scale analysis of the rice-wheat cropping system of Punjab, Pakistan , 2004 .

[52]  W. Bastiaanssen.  SEBAL-based sensible and latent heat fluxes in the irrigated Gediz Basin, Turkey , 2000 .

[53]  H. S. Wolff,et al.  iRun: Horizontal and Vertical Shape of a Region-Based Graph Compression , 2022, Sensors.

[54]  M. Guérif,et al.  Application of SPOT data to wheat yield estimation , 1989 .

[55]  Wim G.M. Bastiaanssen,et al.  Review of measured crop water productivity values for irrigated wheat, rice, cotton and maize , 2004 .

[56]  Marvin E. Jensen,et al.  Operational estimates of reference evapotranspiration , 1989 .

[57]  Anthony Morse,et al.  Evapotranspiration from a satellite-based surface energy balance for the Snake Plain Aquifer in Idaho. , 2002 .

[58]  M. O'connell.  Research Report 2001 , 2001 .

[59]  B. Markham,et al.  Spectral characterization of the LANDSAT Thematic Mapper sensors , 1985 .

[60]  Terry A. Howell,et al.  Conversion between evapotranspiration references and methods. , 2000 .

[61]  B. Markham,et al.  Radiometric properties of U.S. processed Landsat MSS data , 1987 .

[62]  Charlotte de Fraiture,et al.  Indicators for comparing performance of irrigated agricultural systems , 1998 .

[63]  B. Bouman,et al.  Yield and water use of irrigated tropical aerobic rice systems , 2005 .